Abstract: In the context of lepton flavor universality violation (LFUV) studies, we fully derive a general tensor formalism to investigate the role that left- and right-handed neutrino new-physics (NP) terms may have in b -> c tau(nu) over bar (tau) transitions. We present, for several extensions of the Standard Model (SM), numerical results for the Lambda(b) -> Lambda(c)tau(nu) over bar (tau) semileptonic decay, which is expected to be measured with precision at the LHCb. This reaction can be a new source of experimental information that can help to confirm, or maybe rule out, LFUV presently seen in (B) over bar meson decays. The present study analyzes observables that can help in distinguishing between different NP scenarios that otherwise provide very similar results for the branching ratios, which are our currently best hints for LFUV. Since the tau lepton is very short-lived, we consider three subsequent tau-decay modes, two hadronic pi nu(tau) and rho nu(tau) and one leptonic mu(nu) over bar (mu)nu(tau), which have been previously studied for (B) over bar -> D(*) decays. Within the tensor formalism that we have developed in previous works, we re-obtain the expressions for the differential decay width written in terms of visible (experimentally accessible) variables of the massive particle created in the tau decay. There are seven different tau angular and spin asymmetries that are defined in this way and that can be extracted from experiment. Those asymmetries provide observables that can help in constraining possible SM extensions.

Abstract: Context. At GeV energies, the sky is dominated by the interstellar emission from the Galaxy. With limited statistics and spatial resolution, accurately separating point sources is therefore challenging. Aims. Here we present the first application of deep learning based algorithms to automatically detect and classify point sources from gamma-ray data. For concreteness we refer to this approach as AutoSourceID. Methods. To detect point sources, we utilized U-shaped convolutional networks for image segmentation and k-means for source clustering and localization. We also explored the Centroid-Net algorithm, which is designed to find and count objects. Using two algorithms allows for a cross check of the results, while a combination of their results can be used to improve performance. The training data are based on 9.5 years of exposure from The Fermi Large Area Telescope (Fermi-LAT) and we used source properties of active galactic nuclei (AGNs) and pulsars (PSRs) from the fourth Fermi-LAT source catalog in addition to several models of background interstellar emission. The results of the localization algorithm are fed into a classification neural network that is trained to separate the three general source classes (AGNs, PSRs, and FAKE sources). Results. We compared our localization algorithms qualitatively with traditional methods and find them to have similar detection thresholds. We also demonstrate the robustness of our source localization algorithms to modifications in the interstellar emission models, which presents a clear advantage over traditional methods. The classification network is able to discriminate between the three classes with typical accuracy of similar to 70%, as long as balanced data sets are used in classification training. We published online our training data sets and analysis scripts and invite the community to join the data challenge aimed to improve the localization and classification of gamma-ray point sources.

Abstract: Simulating nature and in particular processes in particle physics require expensive computations and sometimes would take much longer than scientists can afford. Here, we explore ways to a solution for this problem by investigating recent advances in generative modeling and present a study for the generation of events from a physical process with deep generative models. The simulation of physical processes requires not only the production of physical events, but to also ensure that these events occur with the correct frequencies. We investigate the feasibility of learning the event generation and the frequency of occurrence with several generative machine learning models to produce events like Monte Carlo generators. We study three processes: a simple two-body decay, the processes e(+)e(-)-> Z -> l(+)l(-) and pp -> tt<mml:mo><overbar></mml:mover> including the decay of the top quarks and a simulation of the detector response. By buffering density information of encoded Monte Carlo events given the encoder of a Variational Autoencoder we are able to construct a prior for the sampling of new events from the decoder that yields distributions that are in very good agreement with real Monte Carlo events and are generated several orders of magnitude faster. Applications of this work include generic density estimation and sampling, targeted event generation via a principal component analysis of encoded ground truth data, anomaly detection and more efficient importance sampling, e.g., for the phase space integration of matrix elements in quantum field theories. Here, the authors report buffered-density variational autoencoders for the generation of physical events. This method is computationally less expensive over other traditional methods and beyond accelerating the data generation process, it can help to steer the generation and to detect anomalies.

Abstract: We present the nonrelativistic limit of the stellar structure equations of Ricci-based gravities, a family of metric-affine theories whose Lagrangian is built via contractions of the metric with the Ricci tensor of an a priori independent connection. We find that this limit is characterized by four parameters that arise in the expansion of several geometric quantities in powers of the stress-energy tensor of the matter fields. We discuss the relevance of this result for the phenomenology of nonrelativistic stars, such as main-sequence stars as well as several substellar objects.

Abstract: Neutron destruction reactions of the cosmic gamma-ray emitter Al-26 are of importance to determine the amount of Al-26 ejected into our galaxy by supernova explosions and for Al-26 production in asymptotic giant branch stars. We performed a new measurement of the Al-26(n, alpha) reaction up to 160-keV neutron energy at the neutron time-of-flight facilities n_TOF at CERN and GELINA at EC-JRC. We provide strengths for ten resonances, six of them for the first time. We use our data to calculate astrophysical reactivities for stellar temperatures up to 0.7 GK. Our results resolve a discrepancy between the two previous direct measurements of this reaction, and indicate higher stellar destruction rates than the most recently recommended reactivity.